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Supervoxel segmentation has strong potential to be incorporated into earlyvideo analysis as superpixel segmentation has in image analysis. However, thereare many plausible supervoxel methods and little understanding as to when andwhere each is most appropriate. Indeed, we are not aware of a singlecomparative study on supervoxel segmentation. To that end, we study sevensupervoxel algorithms, including both off-line and streaming methods, in thecontext of what we consider to be a good supervoxel: namely, spatiotemporaluniformity, object/region boundary detection, region compression and parsimony.For the evaluation we propose a comprehensive suite of seven quality metrics tomeasure these desirable supervoxel characteristics. In addition, we evaluatethe methods in a supervoxel classification task as a proxy for subsequenthigh-level uses of the supervoxels in video analysis. We use six existingbenchmark video datasets with a variety of content-types and dense humanannotations. Our findings have led us to conclusive evidence that thehierarchical graph-based (GBH), segmentation by weighted aggregation (SWA) andtemporal superpixels (TSP) methods are the top-performers among the sevenmethods. They all perform well in terms of segmentation accuracy, but vary inregard to the other desiderata: GBH captures object boundaries best; SWA hasthe best potential for region compression; and TSP achieves the bestundersegmentation error.

LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing